3 reasons why MySQL HeatWave on AWS is better
than Amazon Aurora and Redshift or Snowflake

Here are the top three reasons to choose Oracle MySQL HeatWave on Amazon Web Services (AWS) over Amazon Aurora and Amazon Redshift and over Amazon Aurora and Snowflake.

  1. Simplicity: Transactions, real-time analytics, and machine learning (ML) in one service, without ETL.
  2. Better price-performance: Up to 10X better throughput than Amazon Aurora, 7X better price-performance than Amazon Redshift, and 10X better price-performance than Snowflake on AWS. MySQL Autopilot further improves performance.
  3. Increased data protection: Advanced security features protect data throughout its lifecycle, supporting compliance with regulatory requirements.

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“MySQL HeatWave on AWS simplifies our data platform with a consolidated database for both transaction processing and analytics. We have seen 60-90X faster complex queries compared to AWS RDS and Aurora that generate the real-time analytics we need for targeted, multichannel campaigns.”

Thomas Henz Chief Executive Officer, Johnny Bytes

1. Simplicity

Capability and evidence
MySQL HeatWave on AWS
Amazon Aurora and Redshift
Amazon Aurora and Snowflake
One database service for OLTP and OLAP workloads on AWS
yes

Customers can run OLTP and OLAP workloads in a single database service—without changes to current applications based on MySQL and Aurora. For mixed OLTP and OLAP workloads, applications access a single endpoint using a single SQL syntax.
no

Amazon Aurora is for OLTP; customers need a separate OLAP service, such as Redshift. For mixed OLTP and OLAP workloads, applications must access two different endpoints using two different SQL syntaxes.
no

Amazon Aurora is for OLTP; customers need a separate OLAP service, such as Snowflake. Snowflake’s Unistore is only in preview. For mixed OLTP and OLAP workloads, applications must access two different endpoints using two different SQL syntaxes.
No ETL duplication
yes

The complex, time-consuming, and expensive ETL is eliminated.
no

Single-purpose databases require an ETL process to move data between OLTP and OLAP services. While the "zero-ETL" integration of Aurora and Redshift simplifies the process, data is still replicated between two separate database services for OLTP and OLAP, creating complexity and generating costs.
no

Single-purpose databases require an ETL process to move data between OLTP and OLAP services.
Real-time, secure analytics
yes

Queries always access the most up-to-date data; there’s no data transfer between databases.
no

By the time data goes through ETL and is available in Redshift, it’s already stale. Moving data between stores can present additional security risks. Even with zero-ETL integration between Aurora and Redshift, data moves between stores, and the latency of replicating data between two databases can be problematic for applications requiring real-time analytics.
no

By the time data goes through ETL and is available in Snowflake, it’s already stale. Moving data between stores can present additional security risks.
In-database machine learning
yes

With HeatWave AutoML, developers and data analysts can build, train, deploy, and explain machine learning models within MySQL HeatWave.
no

A separate ML service, such as Amazon SageMaker, is required.
no

A separate ML service, such as Amazon SageMaker, is required.
Explainable data models and predictions
yes

All ML models and predictions are explainable, increasing trust, fairness, causality, and repeatability and helping with regulatory compliance.
no

Predictions from ML models in Aurora ML and Redshift ML aren’t explainable, which may reduce trust, increase risks for bias, and could make regulatory compliance more difficult.
no

Predictions from ML models in Aurora ML aren’t explainable, which may reduce trust, increase risks for bias, and could make regulatory compliance more difficult. Snowflake requires a third-party ML service.
Automated machine learning lifecycle
yes

The ML lifecycle is fully automated, including algorithm selection, intelligent data sampling, feature selection, and hyper-parameter tuning.
no

Aurora ML and Redshift ML require data science expertise to influence the performance, accuracy, and cost of training.
no

Aurora ML requires data science expertise to influence the performance, accuracy, and cost of training. Snowflake requires a third-party ML service.
Interactive ML console
yes

An interactive console lets business analysts build, train, run, and explain ML models using a visual interface—without using SQL commands or any coding.
no

Neither Aurora ML nor Redshift ML provide an interactive console for business analysts to manage ML models. Users are expected to build ML models using SQL.
no

Neither Aurora ML nor Snowflake provide an interactive console for business analysts to manage ML models. Aurora users are expected to build ML models using SQL. Snowflake supports importing ML models created using third-party services.

2. Better price-performance

MySQL HeatWave on AWS delivers up to 10X better throughput than Amazon Aurora with MySQL Autopilot, as demonstrated by a TPC-C benchmark.

TPC-C benchmark

MySQL HeatWave on AWS delivers 7X better price-performance than Amazon Redshift, as demonstrated by a TPC-H benchmark.

TPC-H benchmark

AWS notes that while it doesn’t charge an additional fee for Aurora zero-ETL integration with Redshift, “you pay for existing Amazon Aurora and Amazon Redshift resources used to create and process the change data created as part of a zero-ETL integration. These resources may include additional I/O and storage used by enabling change data capture, Snapshot export costs for the initial data export to seed your Amazon Redshift databases, additional Amazon Redshift storage for storing replicated data, and cross-AZ data transfer costs for moving data from source to target.”


MySQL HeatWave on AWS delivers 10X better price-performance than Snowflake on AWS, as demonstrated by a TPC-H benchmark.

TPC-H pricing benchmark

MySQL Autopilot automates many of the most important and often challenging aspects of achieving high query performance at scale.

Capability and evidence
MySQL HeatWave on AWS
Amazon Aurora and Redshift
Amazon Aurora and Snowflake
Machine learning–powered automation
yes

MySQL Autopilot automates provisioning, data loading, query execution, and failure handling—further improving performance while saving developers and DBAs significant time.
no

Built-in machine learning–powered automation isn’t available. Expertise in both databases and manual operations is required.
no

Built-in machine learning–powered automation isn’t available. Expertise in both databases and manual operations is required.
Automated workload-aware tuning for OLTP
yes

MySQL Autopilot delivers high OLTP throughput that's sustained at high levels of transactions and concurrency.
no

With Aurora the throughput of the system drops at high levels of transactions and concurrency. Redshift can’t be used for OLTP.
no

With Aurora the throughput of the system drops at high levels of transactions and concurrency. Snowflake can’t be used for OLTP.
Automated query performance tuning
yes

MySQL Autopilot learns from the execution of queries to automatically improve the performance of subsequent queries.
no

Query plans aren’t automatically improved using machine learning models.
no

Query plans aren’t automatically improved using machine learning models.
Automated provisioning of the optimal cluster size
yes

MySQL Autopilot autoprovisions the optimal cluster size for a given data set.
no

Developers and DBAs must guess or manually estimate by trial and error the optimal size of the cluster for both databases.
no

Developers and DBAs must guess or manually estimate by trial and error the optimal size of the cluster for both databases.

3. Increased data protection

Capability and evidence
MySQL HeatWave on AWS
Amazon Aurora and Redshift
Amazon Aurora and Snowflake
Digital signatures confirm the authenticity and integrity of messages
yes

Built-in server-side asymmetric encryption with key generation and digital signatures is provided.
no

Built-in server-side asymmetric encryption to implement digital signatures isn’t provided.
no

Built-in server-side asymmetric encryption to implement digital signatures isn’t provided.
Built-in server-side data masking
yes

Data masking and deidentification are built in, helping protect the confidentiality of private data.
no

Data masking and deidentification need to be implemented at the application level.
no

For Aurora data masking and deidentification need to be implemented at the application level.
Built-in server-side database firewall
yes

A built-in server-side database firewall helps protect against various types of attacks, including some database-specific threats such as SQL injection.
no

A built-in server-side database firewall isn’t provided, leaving the database vulnerable to ransomware attacks.
no

A built-in server-side database firewall isn’t provided, leaving the database vulnerable to ransomware attacks.

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